Upload PPO LunarLander-v2 trained agent

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 254.43 +/- 21.29
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7ec8d2fd7760>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ec8d2fd77f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ec8d2fd7880>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ec8d2fd7910>", "_build": "<function ActorCriticPolicy._build at 0x7ec8d2fd79a0>", "forward": "<function ActorCriticPolicy.forward at 0x7ec8d2fd7a30>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ec8d2fd7ac0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ec8d2fd7b50>", "_predict": "<function ActorCriticPolicy._predict at 0x7ec8d2fd7be0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ec8d2fd7c70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ec8d2fd7d00>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ec8d2fd7d90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ec8d2f76040>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 688128, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1706448821522593998, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.31187200000000004, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 166, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu121", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:871bc83e2672b04be57783ecb1a31f40089d5556cb785ab453975dd79b8bfce9
+size 148057

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7ec8d2fd7760>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ec8d2fd77f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ec8d2fd7880>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ec8d2fd7910>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ec8d2fd79a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ec8d2fd7a30>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ec8d2fd7ac0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ec8d2fd7b50>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ec8d2fd7be0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ec8d2fd7c70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ec8d2fd7d00>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ec8d2fd7d90>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ec8d2f76040>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 688128,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1706448821522593998,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.31187200000000004,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 166,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93ebc21eee5387271be8e51bfed9dd20b7a27c7054a9f1182372283f392a794d
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7403f1b16fdbbb74bc4d0dfd66fc788d777ef662a01f09b6ed3aea3615bbccf
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu121
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 254.42777777168598, "std_reward": 21.286458244994424, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-28T13:48:23.263237"}